Monocle per depot

3 feature selection methods: 1) Same feature selection strategy as for the whole dataset (split each depot into T1T2T3 and T4T5, cluster, perform DE tests on clusters, take union of resulting gene lists as features); 2) Genes with high dispersion; 3) Genes used for computing the trajectory of the whole dataset.

cds_peri <- readRDS('output/monocle/180831/peri/monocle_peri_T1T2T3_T4T5_res1/10x-180831-peri-monocle')
cds_peri_disp <- readRDS('output/monocle/180831/peri/monocle_peri_high-dispersion/10x-180831-peri-monocle')
cds_peri_gl <- readRDS('output/monocle/180831/peri/monocle_peri_genelist/10x-180831-peri-monocle')

plot_grid(
  plot_cell_trajectory(cds_peri, color_by='timepoint'),
  plot_cell_trajectory(cds_peri_disp, color_by='timepoint'),
  plot_cell_trajectory(cds_peri_gl, color_by='timepoint'),
  plot_cell_trajectory(cds_peri, color_by='State.old') + scale_color_manual(values=c("#f67770", "#964B00", "orange"), name = "State"),
  plot_cell_trajectory(cds_peri_disp, color_by='State.old') + scale_color_manual(values=c("#f67770", "#964B00", "orange"), name = "State"),
  plot_cell_trajectory(cds_peri_gl, color_by='State.old') + scale_color_manual(values=c("#f67770", "#964B00", "orange"), name = "State"),
  ncol=3
)
cds_supra <- readRDS('output/monocle/180831/supra/monocle_supra_T1T2T3_T4T5_res1/10x-180831-supra-monocle')
cds_supra_disp <- readRDS('output/monocle/180831/supra/monocle_supra_high-dispersion/10x-180831-supra-monocle')
cds_supra_gl <- readRDS('output/monocle/180831/supra/monocle_supra_genelist/10x-180831-supra-monocle')

plot_grid(
  plot_cell_trajectory(cds_supra, color_by='timepoint'),
  plot_cell_trajectory(cds_supra_disp, color_by='timepoint'),
  plot_cell_trajectory(cds_supra_gl, color_by='timepoint'),
  plot_cell_trajectory(cds_supra, color_by='State.old') + scale_color_manual(values=c("#f67770", "#964B00", "orange"), name = "State"),
  plot_cell_trajectory(cds_supra_disp, color_by='State.old') + scale_color_manual(values=c("#f67770", "#964B00", "orange"), name = "State"),
  plot_cell_trajectory(cds_supra_gl, color_by='State.old') + scale_color_manual(values=c("#f67770", "#964B00", "orange"), name = "State"),
  ncol=3
)
cds_subq <- readRDS('output/monocle/180831/subq/monocle_subq_T1T2T3_T4T5_res1/10x-180831-subq-monocle')
cds_subq_disp <- readRDS('output/monocle/180831/subq/monocle_subq_high-dispersion/10x-180831-subq-monocle')
cds_subq_gl <- readRDS('output/monocle/180831/subq/monocle_subq_genelist/10x-180831-subq-monocle')

plot_grid(
  plot_cell_trajectory(cds_subq, color_by='timepoint'),
  plot_cell_trajectory(cds_subq_disp, color_by='timepoint'),
  plot_cell_trajectory(cds_subq_gl, color_by='timepoint'),
  plot_cell_trajectory(cds_subq, color_by='State.old') + scale_color_manual(values=c("#f67770", "#964B00", "orange"), name = "State"),
  plot_cell_trajectory(cds_subq_disp, color_by='State.old') + scale_color_manual(values=c("#f67770", "#964B00", "orange"), name = "State"),
  plot_cell_trajectory(cds_subq_gl, color_by='State.old') + scale_color_manual(values=c("#f67770", "#964B00", "orange"), name = "State"),
  ncol=3
)
cds_visce <- readRDS('output/monocle/180831/visce/monocle_visce_T1T2T3_T4T5_res1/10x-180831-visce-monocle')
cds_visce_disp <- readRDS('output/monocle/180831/visce/monocle_visce_high-dispersion/10x-180831-visce-monocle')
cds_visce_gl <- readRDS('output/monocle/180831/visce/monocle_visce_genelist/10x-180831-visce-monocle')

plot_grid(
  plot_cell_trajectory(cds_visce, color_by='timepoint'),
  plot_cell_trajectory(cds_visce_disp, color_by='timepoint'),
  plot_cell_trajectory(cds_visce_gl, color_by='timepoint'),
  plot_cell_trajectory(cds_visce, color_by='State.old') + scale_color_manual(values=c("#f67770", "#964B00", "orange"), name = "State"),
  plot_cell_trajectory(cds_visce_disp, color_by='State.old') + scale_color_manual(values=c("#f67770", "#964B00", "orange"), name = "State"),
  plot_cell_trajectory(cds_visce_gl, color_by='State.old') + scale_color_manual(values=c("#f67770", "#964B00", "orange"), name = "State"),
  ncol=3
)
plot_grid(
  plot_cell_trajectory(cds_peri, color_by='State.old') + scale_color_manual(values=c("#f67770", "#964B00", "orange"), name = "State"),
  plot_cell_trajectory(cds_supra, color_by='State.old') + scale_color_manual(values=c("#f67770", "#964B00", "orange"), name = "State"),
  plot_cell_trajectory(cds_subq, color_by='State.old') + scale_color_manual(values=c("#f67770", "#964B00", "orange"), name = "State"),
  plot_cell_trajectory(cds_visce, color_by='State.old') + scale_color_manual(values=c("#f67770", "#964B00", "orange"), name = "State"),
  ncol=2
)
plot_grid(
  plot_cell_trajectory(cds_peri, color_by='State'),
  plot_cell_trajectory(cds_supra, color_by='State'), 
  plot_cell_trajectory(cds_subq, color_by='State'), 
  plot_cell_trajectory(cds_visce, color_by='State'),
  ncol=2
)
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